{"id":36997,"url":"https://alion.io/job/granica-research-scientist-large-tabular-models-ltms","title":"Research Scientist – Large Tabular Models (LTMs)","company":{"id":8292,"name":"Granica","domain":"granica.ai","url":"https://alion.io/company/granica","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":70,"open_postings":10,"ghost_share":0.5,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-24T06:02:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":160000,"max":240000,"currency":"USD","period":"year","gross":null,"usd_annual":240000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false}],"status":"live","first_seen_at":"2026-07-02T00:26:32Z","employer_posted_date":"2026-07-02","last_verified_at":"2026-10-24T20:33:16Z","board_verified":true,"closed_at":null,"days_open":114,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":114},"description":"About Granica\nGranica is building the efficiency and intelligence layer for enterprise AI.\nCrunch makes massive enterprise data cheaper and easier to operate.\n\nLarge Tabular Models learn from structured data to support shared intelligence across many capabilities.\n\nMyelin makes long-running AI agents more efficient and durable.\n\nGranica has processed hundreds of petabytes of tabular data in production, and our research is led by Stanford Professor Andrea Montanari.\nLocation: Mountain View, CA (On-site)\nOverview\nMost of today's generative AI is built for text, images, and video.\nEnterprise data isn't.\nThe world's most valuable data lives in tables: customer records, transactions, financial systems, telemetry, operational data, and business workflows. Today's generative AI stack wasn't designed to learn efficiently from this kind of information.\nAt Granica, we're building Large Tabular Models (LTMs) -foundation models that learn natively from structured and relational enterprise data.\nOur research is led by Prof. Andrea Montanari (Stanford) and focuses on one central question:\nHow can we build generative AI that learns efficiently from tabular data?\nThat requires solving problems well beyond model architecture, including intelligent data selection, dataset augmentation, representation learning, and information-preserving compression.\nIf you're excited about inventing the algorithms that make Large Tabular Models possible, we'd love to talk.\nWhat You'll Work On\nDevelop new machine learning algorithms for Large Tabular Models.\n\nResearch methods for selecting, augmenting, and compressing training data without losing information.\n\nBuild representation learning techniques for structured and relational datasets.\n\nPrototype and evaluate new approaches for generative modeling over enterprise data.\n\nDesign rigorous experiments and benchmarks to measure progress.\n\nCollaborate closely with Prof. Andrea Montanari and Granica's research team to translate research into production systems.\n\nWhat We're Looking For\nPhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field preferred; equivalent research and/or industry experience and demonstrated contributions to advancing state-of-the-art AI models will also be considered.\n\nStrong research record in machine learning.\n\nExperience developing new models or learning algorithms.\n\nHands-on experience with PyTorch or JAX.\n\nStrong programming skills in Python.\n\nAbility to turn research ideas into working systems.\n\nExperience in structured learning, representation learning, generative modeling, probabilistic modeling, statistical learning, or scalable ML systems is particularly relevant.\n\nBonus\nResearch on tabular, relational, or graph data.\n\nExperience with diffusion or other generative modeling approaches.\n\nPublications at NeurIPS, ICML, ICLR, COLT, KDD, or related venues.\n\nOpen-source or production ML systems experience.\n\nCompensation & Benefits\nCompetitive salary, meaningful equity, and performance bonus for top performers\n\n401(k) with company match, comprehensive health coverage, and unlimited PTO\n\nDaily catered meals in our Mountain View office\n\nSupport for research, publication, and conference participation\n\nAt Granica, you'll help build the next generation of enterprise AI -from exabyte-scale data infrastructure, Large Tabular Models (LTMs), and stateful AI agents. 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